Discovering Consumers Purchasing Behavior Based on Efficient Association Rules
Mining generalized association rules between items in the presence of taxonomies has been recognized as an important model in data mining. The classic Apriori itemset generation works in the presence of taxonomy but fails in the case of non-uniform minimum supports. In this paper, we extended the scope of mining generalized association rules in the presence of taxonomies to allow any form of user-specified multiple minimum supports. This method considers taxonomy of itemset, and can discover some deviations or exceptions that are more interesting but much less supported than general trends. Finally, the algorithms is validated by the example of transaction database. The result indicates this algorithm is successful in discovering consumers purchasing behavior by user specifing different minimum support for different items.
consumer minimum support association rule item purchasing behavior
Chong Wang Yanqing Wang
Business School Huaihai Institute of Technology Lianyungang, 222000, China School of Management Harb School of Management Harbin Institute of Technology Harbin, 150001,China
国际会议
上海
英文
968-972
2011-07-26（万方平台首次上网日期，不代表论文的发表时间）